Overview

Dataset statistics

Number of variables27
Number of observations167278
Missing cells911893
Missing cells (%)20.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory245.2 MiB
Average record size in memory1.5 KiB

Variable types

Text15
Categorical6
DateTime2
Boolean2
Numeric2

Alerts

EDUCATION_LEVEL_REQUIRED is highly overall correlated with VISA_CLASSHigh correlation
EXPERIENCE_REQUIRED_NUM_MONTHS is highly overall correlated with VISA_CLASS and 1 other fieldsHigh correlation
EXPERIENCE_REQUIRED_Y_N is highly overall correlated with VISA_CLASSHigh correlation
FULL_TIME_POSITION_Y_N is highly overall correlated with PAID_WAGE_SUBMITTED_UNIT and 1 other fieldsHigh correlation
PAID_WAGE_SUBMITTED_UNIT is highly overall correlated with FULL_TIME_POSITION_Y_N and 1 other fieldsHigh correlation
PREVAILING_WAGE_SUBMITTED_UNIT is highly overall correlated with FULL_TIME_POSITION_Y_N and 1 other fieldsHigh correlation
VISA_CLASS is highly overall correlated with EDUCATION_LEVEL_REQUIRED and 2 other fieldsHigh correlation
order is highly overall correlated with EXPERIENCE_REQUIRED_NUM_MONTHSHigh correlation
CASE_STATUS is highly imbalanced (60.2%)Imbalance
PREVAILING_WAGE_SUBMITTED_UNIT is highly imbalanced (86.5%)Imbalance
PAID_WAGE_SUBMITTED_UNIT is highly imbalanced (86.4%)Imbalance
FULL_TIME_POSITION_Y_N is highly imbalanced (84.2%)Imbalance
VISA_CLASS is highly imbalanced (81.0%)Imbalance
EDUCATION_LEVEL_REQUIRED has 156215 (93.4%) missing valuesMissing
COLLEGE_MAJOR_REQUIRED has 156227 (93.4%) missing valuesMissing
EXPERIENCE_REQUIRED_Y_N has 156185 (93.4%) missing valuesMissing
EXPERIENCE_REQUIRED_NUM_MONTHS has 162313 (97.0%) missing valuesMissing
COUNTRY_OF_CITIZENSHIP has 156185 (93.4%) missing valuesMissing
WORK_POSTAL_CODE has 113604 (67.9%) missing valuesMissing
FULL_TIME_POSITION_Y_N has 11093 (6.6%) missing valuesMissing
order is uniformly distributedUniform
CASE_NUMBER has unique valuesUnique
order has unique valuesUnique

Reproduction

Analysis started2026-01-19 13:19:42.032721
Analysis finished2026-01-19 13:19:54.426296
Duration12.39 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

CASE_NUMBER
Text

Unique 

Distinct167278
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
2026-01-19T14:19:54.691960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length18
Median length18
Mean length17.668426
Min length13

Characters and Unicode

Total characters2955539
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique167278 ?
Unique (%)100.0%

Sample

1st rowI-200-14073-248840
2nd rowA-15061-55212
3rd rowI-200-13256-001092
4th rowI-200-14087-353657
5th rowI-203-14259-128844
ValueCountFrequency (%)
i-200-14073-2488401
 
< 0.1%
i-200-13311-4406031
 
< 0.1%
i-200-13273-9005481
 
< 0.1%
i-200-13274-8080581
 
< 0.1%
i-200-14069-4009501
 
< 0.1%
i-200-13256-0010921
 
< 0.1%
i-200-14087-3536571
 
< 0.1%
i-203-14259-1288441
 
< 0.1%
i-200-14092-4832721
 
< 0.1%
i-200-13084-4872921
 
< 0.1%
Other values (167268)167268
> 99.9%
2026-01-19T14:19:55.021701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0523625
17.7%
-490741
16.6%
1339562
11.5%
2328811
11.1%
3201849
 
6.8%
4196955
 
6.7%
5163600
 
5.5%
I156185
 
5.3%
7144688
 
4.9%
6139716
 
4.7%
Other values (4)269807
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2955539
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0523625
17.7%
-490741
16.6%
1339562
11.5%
2328811
11.1%
3201849
 
6.8%
4196955
 
6.7%
5163600
 
5.5%
I156185
 
5.3%
7144688
 
4.9%
6139716
 
4.7%
Other values (4)269807
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2955539
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0523625
17.7%
-490741
16.6%
1339562
11.5%
2328811
11.1%
3201849
 
6.8%
4196955
 
6.7%
5163600
 
5.5%
I156185
 
5.3%
7144688
 
4.9%
6139716
 
4.7%
Other values (4)269807
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2955539
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0523625
17.7%
-490741
16.6%
1339562
11.5%
2328811
11.1%
3201849
 
6.8%
4196955
 
6.7%
5163600
 
5.5%
I156185
 
5.3%
7144688
 
4.9%
6139716
 
4.7%
Other values (4)269807
9.1%

CASE_STATUS
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.7 MiB
certified
140031 
certified-withdrawn
14146 
withdrawn
 
5602
denied
 
4273
certified-expired
 
3226

Length

Max length19
Median length9
Mean length9.9233073
Min length6

Characters and Unicode

Total characters1659951
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdenied
2nd rowdenied
3rd rowdenied
4th rowdenied
5th rowdenied

Common Values

ValueCountFrequency (%)
certified140031
83.7%
certified-withdrawn14146
 
8.5%
withdrawn5602
 
3.3%
denied4273
 
2.6%
certified-expired3226
 
1.9%

Length

2026-01-19T14:19:55.141696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-19T14:19:55.277539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
certified140031
83.7%
certified-withdrawn14146
 
8.5%
withdrawn5602
 
3.3%
denied4273
 
2.6%
certified-expired3226
 
1.9%

Most occurring characters

ValueCountFrequency (%)
i342053
20.6%
e329804
19.9%
d188923
11.4%
r180377
10.9%
t177151
10.7%
c157403
9.5%
f157403
9.5%
w39496
 
2.4%
n24021
 
1.4%
h19748
 
1.2%
Other values (4)43572
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1659951
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i342053
20.6%
e329804
19.9%
d188923
11.4%
r180377
10.9%
t177151
10.7%
c157403
9.5%
f157403
9.5%
w39496
 
2.4%
n24021
 
1.4%
h19748
 
1.2%
Other values (4)43572
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1659951
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i342053
20.6%
e329804
19.9%
d188923
11.4%
r180377
10.9%
t177151
10.7%
c157403
9.5%
f157403
9.5%
w39496
 
2.4%
n24021
 
1.4%
h19748
 
1.2%
Other values (4)43572
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1659951
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i342053
20.6%
e329804
19.9%
d188923
11.4%
r180377
10.9%
t177151
10.7%
c157403
9.5%
f157403
9.5%
w39496
 
2.4%
n24021
 
1.4%
h19748
 
1.2%
Other values (4)43572
 
2.6%
Distinct1769
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Minimum2008-07-16 00:00:00
Maximum2015-06-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-19T14:19:55.406049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T14:19:55.521832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct874
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Minimum2011-10-03 00:00:00
Maximum2015-06-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-19T14:19:55.635243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T14:19:55.767280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct23773
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Memory size12.7 MiB
2026-01-19T14:19:56.010469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length70
Median length59
Mean length22.597837
Min length2

Characters and Unicode

Total characters3780121
Distinct characters89
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11002 ?
Unique (%)6.6%

Sample

1st rowADVANCED TECHNOLOGY GROUP USA, INC.
2nd rowSAN FRANCISCO STATE UNIVERSITY
3rd rowCAROUSEL SCHOOL
4th rowHARLINGEN CONSOLIDATED INDEPENDENT SCHOOL DISTRICT
5th rowSIGNAL SCIENCES CORPORATION
ValueCountFrequency (%)
inc91608
 
17.3%
llc16521
 
3.1%
university16301
 
3.1%
corporation13834
 
2.6%
of12578
 
2.4%
technologies9998
 
1.9%
systems8002
 
1.5%
solutions7903
 
1.5%
school7119
 
1.3%
services6525
 
1.2%
Other values (17402)338991
64.0%
2026-01-19T14:19:56.433786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
362355
 
9.6%
I351701
 
9.3%
N310465
 
8.2%
E279696
 
7.4%
O279110
 
7.4%
C276296
 
7.3%
T241073
 
6.4%
S232804
 
6.2%
A214875
 
5.7%
R188413
 
5.0%
Other values (79)1043333
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)3780121
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
362355
 
9.6%
I351701
 
9.3%
N310465
 
8.2%
E279696
 
7.4%
O279110
 
7.4%
C276296
 
7.3%
T241073
 
6.4%
S232804
 
6.2%
A214875
 
5.7%
R188413
 
5.0%
Other values (79)1043333
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3780121
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
362355
 
9.6%
I351701
 
9.3%
N310465
 
8.2%
E279696
 
7.4%
O279110
 
7.4%
C276296
 
7.3%
T241073
 
6.4%
S232804
 
6.2%
A214875
 
5.7%
R188413
 
5.0%
Other values (79)1043333
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3780121
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
362355
 
9.6%
I351701
 
9.3%
N310465
 
8.2%
E279696
 
7.4%
O279110
 
7.4%
C276296
 
7.3%
T241073
 
6.4%
S232804
 
6.2%
A214875
 
5.7%
R188413
 
5.0%
Other values (79)1043333
27.6%
Distinct22509
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size10.2 MiB
2026-01-19T14:19:56.682609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.8641603
Min length2

Characters and Unicode

Total characters1148223
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9139 ?
Unique (%)5.5%

Sample

1st row6217100
2nd row5067600
3rd row4947000
4th row251052.00
5th row84573.00
ValueCountFrequency (%)
98675908
 
0.5%
88254.00839
 
0.5%
98675.00796
 
0.5%
109762.00779
 
0.5%
97219.00763
 
0.5%
94162.00606
 
0.4%
80746.00603
 
0.4%
93267.00601
 
0.4%
62379.00497
 
0.3%
116605489
 
0.3%
Other values (22483)160397
95.9%
2026-01-19T14:19:57.050162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0290460
25.3%
.103822
 
9.0%
695532
 
8.3%
191698
 
8.0%
789849
 
7.8%
486077
 
7.5%
882774
 
7.2%
978968
 
6.9%
278434
 
6.8%
578157
 
6.8%
Other values (2)72452
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1148223
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0290460
25.3%
.103822
 
9.0%
695532
 
8.3%
191698
 
8.0%
789849
 
7.8%
486077
 
7.5%
882774
 
7.2%
978968
 
6.9%
278434
 
6.8%
578157
 
6.8%
Other values (2)72452
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1148223
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0290460
25.3%
.103822
 
9.0%
695532
 
8.3%
191698
 
8.0%
789849
 
7.8%
486077
 
7.5%
882774
 
7.2%
978968
 
6.9%
278434
 
6.8%
578157
 
6.8%
Other values (2)72452
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1148223
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0290460
25.3%
.103822
 
9.0%
695532
 
8.3%
191698
 
8.0%
789849
 
7.8%
486077
 
7.5%
882774
 
7.2%
978968
 
6.9%
278434
 
6.8%
578157
 
6.8%
Other values (2)72452
 
6.3%

PREVAILING_WAGE_SUBMITTED_UNIT
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
year
158413 
hour
 
8500
month
 
292
week
 
59
bi-weekly
 
14

Length

Max length9
Median length4
Mean length4.0021641
Min length4

Characters and Unicode

Total characters669474
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyear
2nd rowyear
3rd rowyear
4th rowmonth
5th rowbi-weekly

Common Values

ValueCountFrequency (%)
year158413
94.7%
hour8500
 
5.1%
month292
 
0.2%
week59
 
< 0.1%
bi-weekly14
 
< 0.1%

Length

2026-01-19T14:19:57.175654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-19T14:19:57.298624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
year158413
94.7%
hour8500
 
5.1%
month292
 
0.2%
week59
 
< 0.1%
bi-weekly14
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r166913
24.9%
e158559
23.7%
y158427
23.7%
a158413
23.7%
h8792
 
1.3%
o8792
 
1.3%
u8500
 
1.3%
m292
 
< 0.1%
n292
 
< 0.1%
t292
 
< 0.1%
Other values (6)202
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)669474
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r166913
24.9%
e158559
23.7%
y158427
23.7%
a158413
23.7%
h8792
 
1.3%
o8792
 
1.3%
u8500
 
1.3%
m292
 
< 0.1%
n292
 
< 0.1%
t292
 
< 0.1%
Other values (6)202
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)669474
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r166913
24.9%
e158559
23.7%
y158427
23.7%
a158413
23.7%
h8792
 
1.3%
o8792
 
1.3%
u8500
 
1.3%
m292
 
< 0.1%
n292
 
< 0.1%
t292
 
< 0.1%
Other values (6)202
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)669474
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r166913
24.9%
e158559
23.7%
y158427
23.7%
a158413
23.7%
h8792
 
1.3%
o8792
 
1.3%
u8500
 
1.3%
m292
 
< 0.1%
n292
 
< 0.1%
t292
 
< 0.1%
Other values (6)202
 
< 0.1%
Distinct23031
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
2026-01-19T14:19:57.546710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.4854015
Min length1

Characters and Unicode

Total characters917587
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13615 ?
Unique (%)8.1%

Sample

1st row62171
2nd row91440
3rd row49470
4th row43800
5th row170000
ValueCountFrequency (%)
600006967
 
4.2%
650003887
 
2.3%
700003580
 
2.1%
900003154
 
1.9%
1000003094
 
1.8%
800003091
 
1.8%
750002825
 
1.7%
850002587
 
1.5%
1100002455
 
1.5%
1050002392
 
1.4%
Other values (23021)133246
79.7%
2026-01-19T14:19:57.907833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0386578
42.1%
182983
 
9.0%
569516
 
7.6%
666452
 
7.2%
756802
 
6.2%
853156
 
5.8%
250188
 
5.5%
444240
 
4.8%
944075
 
4.8%
341815
 
4.6%
Other values (2)21782
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)917587
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0386578
42.1%
182983
 
9.0%
569516
 
7.6%
666452
 
7.2%
756802
 
6.2%
853156
 
5.8%
250188
 
5.5%
444240
 
4.8%
944075
 
4.8%
341815
 
4.6%
Other values (2)21782
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)917587
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0386578
42.1%
182983
 
9.0%
569516
 
7.6%
666452
 
7.2%
756802
 
6.2%
853156
 
5.8%
250188
 
5.5%
444240
 
4.8%
944075
 
4.8%
341815
 
4.6%
Other values (2)21782
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)917587
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0386578
42.1%
182983
 
9.0%
569516
 
7.6%
666452
 
7.2%
756802
 
6.2%
853156
 
5.8%
250188
 
5.5%
444240
 
4.8%
944075
 
4.8%
341815
 
4.6%
Other values (2)21782
 
2.4%

PAID_WAGE_SUBMITTED_UNIT
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
year
158428 
hour
 
8397
month
 
358
week
 
62
bi-weekly
 
33

Length

Max length9
Median length4
Mean length4.0031265
Min length4

Characters and Unicode

Total characters669635
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyear
2nd rowyear
3rd rowyear
4th rowyear
5th rowyear

Common Values

ValueCountFrequency (%)
year158428
94.7%
hour8397
 
5.0%
month358
 
0.2%
week62
 
< 0.1%
bi-weekly33
 
< 0.1%

Length

2026-01-19T14:19:58.031239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-19T14:19:58.152733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
year158428
94.7%
hour8397
 
5.0%
month358
 
0.2%
week62
 
< 0.1%
bi-weekly33
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r166825
24.9%
e158618
23.7%
y158461
23.7%
a158428
23.7%
h8755
 
1.3%
o8755
 
1.3%
u8397
 
1.3%
m358
 
0.1%
n358
 
0.1%
t358
 
0.1%
Other values (6)322
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)669635
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r166825
24.9%
e158618
23.7%
y158461
23.7%
a158428
23.7%
h8755
 
1.3%
o8755
 
1.3%
u8397
 
1.3%
m358
 
0.1%
n358
 
0.1%
t358
 
0.1%
Other values (6)322
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)669635
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r166825
24.9%
e158618
23.7%
y158461
23.7%
a158428
23.7%
h8755
 
1.3%
o8755
 
1.3%
u8397
 
1.3%
m358
 
0.1%
n358
 
0.1%
t358
 
0.1%
Other values (6)322
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)669635
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r166825
24.9%
e158618
23.7%
y158461
23.7%
a158428
23.7%
h8755
 
1.3%
o8755
 
1.3%
u8397
 
1.3%
m358
 
0.1%
n358
 
0.1%
t358
 
0.1%
Other values (6)322
 
< 0.1%
Distinct12589
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size12.6 MiB
2026-01-19T14:19:58.384056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length94
Median length91
Mean length21.678852
Min length7

Characters and Unicode

Total characters3626395
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8218 ?
Unique (%)4.9%

Sample

1st rowSOFTWARE ENGINEER
2nd rowAssistant Professor of Marketing
3rd rowSPECIAL EDUCATION TEACHER
4th rowSCIENCE TEACHER
5th rowSENIOR SOFTWARE ENGINEER
ValueCountFrequency (%)
software99217
22.5%
engineer96969
22.0%
analyst31579
 
7.2%
business27846
 
6.3%
senior19028
 
4.3%
assistant18933
 
4.3%
professor18531
 
4.2%
teacher13515
 
3.1%
data5241
 
1.2%
5235
 
1.2%
Other values (4225)104126
23.7%
2026-01-19T14:19:58.754867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E534851
14.7%
S372932
10.3%
N326319
9.0%
R288600
 
8.0%
A277283
 
7.6%
273097
 
7.5%
T229430
 
6.3%
I218543
 
6.0%
O186451
 
5.1%
F122777
 
3.4%
Other values (65)796112
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)3626395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E534851
14.7%
S372932
10.3%
N326319
9.0%
R288600
 
8.0%
A277283
 
7.6%
273097
 
7.5%
T229430
 
6.3%
I218543
 
6.0%
O186451
 
5.1%
F122777
 
3.4%
Other values (65)796112
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3626395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E534851
14.7%
S372932
10.3%
N326319
9.0%
R288600
 
8.0%
A277283
 
7.6%
273097
 
7.5%
T229430
 
6.3%
I218543
 
6.0%
O186451
 
5.1%
F122777
 
3.4%
Other values (65)796112
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3626395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E534851
14.7%
S372932
10.3%
N326319
9.0%
R288600
 
8.0%
A277283
 
7.6%
273097
 
7.5%
T229430
 
6.3%
I218543
 
6.0%
O186451
 
5.1%
F122777
 
3.4%
Other values (65)796112
22.0%
Distinct4888
Distinct (%)2.9%
Missing3
Missing (%)< 0.1%
Memory size10.6 MiB
2026-01-19T14:19:58.966991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length44
Median length26
Mean length9.1463937
Min length2

Characters and Unicode

Total characters1529963
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1615 ?
Unique (%)1.0%

Sample

1st rowBLOOMINGTON
2nd rowSAN FRANCISCO
3rd rowLOS ANGELES
4th rowHARLINGEN CISD
5th rowPORTLAND
ValueCountFrequency (%)
san16892
 
7.2%
new7743
 
3.3%
view7459
 
3.2%
mountain7418
 
3.2%
york7116
 
3.0%
francisco6847
 
2.9%
city5776
 
2.5%
jose3757
 
1.6%
santa3661
 
1.6%
diego3332
 
1.4%
Other values (3409)163923
70.1%
2026-01-19T14:19:59.294103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A149438
 
9.8%
N137654
 
9.0%
O120134
 
7.9%
E110301
 
7.2%
I97159
 
6.4%
S96910
 
6.3%
L89550
 
5.9%
R87077
 
5.7%
T83359
 
5.4%
66665
 
4.4%
Other values (56)491716
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1529963
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A149438
 
9.8%
N137654
 
9.0%
O120134
 
7.9%
E110301
 
7.2%
I97159
 
6.4%
S96910
 
6.3%
L89550
 
5.9%
R87077
 
5.7%
T83359
 
5.4%
66665
 
4.4%
Other values (56)491716
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1529963
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A149438
 
9.8%
N137654
 
9.0%
O120134
 
7.9%
E110301
 
7.2%
I97159
 
6.4%
S96910
 
6.3%
L89550
 
5.9%
R87077
 
5.7%
T83359
 
5.4%
66665
 
4.4%
Other values (56)491716
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1529963
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A149438
 
9.8%
N137654
 
9.0%
O120134
 
7.9%
E110301
 
7.2%
I97159
 
6.4%
S96910
 
6.3%
L89550
 
5.9%
R87077
 
5.7%
T83359
 
5.4%
66665
 
4.4%
Other values (56)491716
32.1%

EDUCATION_LEVEL_REQUIRED
Categorical

High correlation  Missing 

Distinct6
Distinct (%)0.1%
Missing156215
Missing (%)93.4%
Memory size10.2 MiB
Master's
5550 
Bachelor's
3938 
Doctorate
1181 
Other
 
366
Associate's
 
21

Length

Max length11
Median length8
Mean length8.727018
Min length5

Characters and Unicode

Total characters96547
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDoctorate
2nd rowBachelor's
3rd rowBachelor's
4th rowMaster's
5th rowOther

Common Values

ValueCountFrequency (%)
Master's5550
 
3.3%
Bachelor's3938
 
2.4%
Doctorate1181
 
0.7%
Other366
 
0.2%
Associate's21
 
< 0.1%
High School7
 
< 0.1%
(Missing)156215
93.4%

Length

2026-01-19T14:19:59.413122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-19T14:19:59.530334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
master's5550
50.1%
bachelor's3938
35.6%
doctorate1181
 
10.7%
other366
 
3.3%
associate's21
 
0.2%
high7
 
0.1%
school7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
s15101
15.6%
e11056
11.5%
r11035
11.4%
a10690
11.1%
'9509
9.8%
t8299
8.6%
o6335
6.6%
M5550
 
5.7%
c5147
 
5.3%
h4318
 
4.5%
Other values (10)9507
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)96547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s15101
15.6%
e11056
11.5%
r11035
11.4%
a10690
11.1%
'9509
9.8%
t8299
8.6%
o6335
6.6%
M5550
 
5.7%
c5147
 
5.3%
h4318
 
4.5%
Other values (10)9507
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)96547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s15101
15.6%
e11056
11.5%
r11035
11.4%
a10690
11.1%
'9509
9.8%
t8299
8.6%
o6335
6.6%
M5550
 
5.7%
c5147
 
5.3%
h4318
 
4.5%
Other values (10)9507
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)96547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s15101
15.6%
e11056
11.5%
r11035
11.4%
a10690
11.1%
'9509
9.8%
t8299
8.6%
o6335
6.6%
M5550
 
5.7%
c5147
 
5.3%
h4318
 
4.5%
Other values (10)9507
9.8%
Distinct3261
Distinct (%)29.5%
Missing156227
Missing (%)93.4%
Memory size5.8 MiB
2026-01-19T14:19:59.701372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length100
Median length85
Mean length41.916659
Min length2

Characters and Unicode

Total characters463221
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2529 ?
Unique (%)22.9%

Sample

1st rowmarketing
2nd rowcomputer science, electrical engineering
3rd rowcomputer science, electrical engineering, or a related field
4th rowelectronic eng, computer sci, computer eng, imaging or related field
5th rowmedicine
ValueCountFrequency (%)
or8561
13.7%
computer7571
 
12.1%
science5617
 
9.0%
related5404
 
8.7%
engineering3832
 
6.1%
field3670
 
5.9%
comp2137
 
3.4%
eng1725
 
2.8%
sci1631
 
2.6%
electrical1627
 
2.6%
Other values (1281)20649
33.1%
2026-01-19T14:20:00.019090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e65971
14.2%
51431
11.1%
c36868
 
8.0%
i33542
 
7.2%
n32352
 
7.0%
r31978
 
6.9%
o25681
 
5.5%
t23671
 
5.1%
s19143
 
4.1%
l18457
 
4.0%
Other values (38)124127
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)463221
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e65971
14.2%
51431
11.1%
c36868
 
8.0%
i33542
 
7.2%
n32352
 
7.0%
r31978
 
6.9%
o25681
 
5.5%
t23671
 
5.1%
s19143
 
4.1%
l18457
 
4.0%
Other values (38)124127
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)463221
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e65971
14.2%
51431
11.1%
c36868
 
8.0%
i33542
 
7.2%
n32352
 
7.0%
r31978
 
6.9%
o25681
 
5.5%
t23671
 
5.1%
s19143
 
4.1%
l18457
 
4.0%
Other values (38)124127
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)463221
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e65971
14.2%
51431
11.1%
c36868
 
8.0%
i33542
 
7.2%
n32352
 
7.0%
r31978
 
6.9%
o25681
 
5.5%
t23671
 
5.1%
s19143
 
4.1%
l18457
 
4.0%
Other values (38)124127
26.8%

EXPERIENCE_REQUIRED_Y_N
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing156185
Missing (%)93.4%
Memory size326.8 KiB
False
 
6139
True
 
4954
(Missing)
156185 
ValueCountFrequency (%)
False6139
 
3.7%
True4954
 
3.0%
(Missing)156185
93.4%
2026-01-19T14:20:00.137617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

EXPERIENCE_REQUIRED_NUM_MONTHS
Real number (ℝ)

High correlation  Missing 

Distinct26
Distinct (%)0.5%
Missing162313
Missing (%)97.0%
Infinite0
Infinite (%)0.0%
Mean34.692044
Minimum0
Maximum144
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2026-01-19T14:20:00.226426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q112
median24
Q360
95-th percentile60
Maximum144
Range144
Interquartile range (IQR)48

Descriptive statistics

Standard deviation22.317783
Coefficient of variation (CV)0.64331126
Kurtosis-0.7543936
Mean34.692044
Median Absolute Deviation (MAD)12
Skewness0.40818635
Sum172246
Variance498.08343
MonotonicityNot monotonic
2026-01-19T14:20:00.446791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
601604
 
1.0%
241105
 
0.7%
12938
 
0.6%
36561
 
0.3%
6454
 
0.3%
4878
 
< 0.1%
7246
 
< 0.1%
8439
 
< 0.1%
9628
 
< 0.1%
320
 
< 0.1%
Other values (16)92
 
0.1%
(Missing)162313
97.0%
ValueCountFrequency (%)
011
 
< 0.1%
115
 
< 0.1%
23
 
< 0.1%
320
 
< 0.1%
418
 
< 0.1%
52
 
< 0.1%
6454
0.3%
81
 
< 0.1%
99
 
< 0.1%
103
 
< 0.1%
ValueCountFrequency (%)
1441
 
< 0.1%
1209
 
< 0.1%
1084
 
< 0.1%
9628
 
< 0.1%
8439
 
< 0.1%
7246
 
< 0.1%
601604
1.0%
4878
 
< 0.1%
421
 
< 0.1%
36561
 
0.3%
Distinct134
Distinct (%)1.2%
Missing156185
Missing (%)93.4%
Memory size5.4 MiB
2026-01-19T14:20:00.643861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length5
Mean length5.8120436
Min length4

Characters and Unicode

Total characters64473
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)0.3%

Sample

1st rowIRAN
2nd rowINDIA
3rd rowINDIA
4th rowFRANCE
5th rowJAPAN
ValueCountFrequency (%)
india6587
56.7%
china1101
 
9.5%
canada425
 
3.7%
philippines300
 
2.6%
south284
 
2.4%
korea269
 
2.3%
mexico255
 
2.2%
taiwan110
 
0.9%
russia109
 
0.9%
france100
 
0.9%
Other values (147)2083
 
17.9%
2026-01-19T14:20:00.963151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I16953
26.3%
A11825
18.3%
N10011
15.5%
D7425
11.5%
C2137
 
3.3%
E2124
 
3.3%
H1840
 
2.9%
R1409
 
2.2%
O1334
 
2.1%
P1327
 
2.1%
Other values (20)8088
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)64473
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I16953
26.3%
A11825
18.3%
N10011
15.5%
D7425
11.5%
C2137
 
3.3%
E2124
 
3.3%
H1840
 
2.9%
R1409
 
2.2%
O1334
 
2.1%
P1327
 
2.1%
Other values (20)8088
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)64473
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I16953
26.3%
A11825
18.3%
N10011
15.5%
D7425
11.5%
C2137
 
3.3%
E2124
 
3.3%
H1840
 
2.9%
R1409
 
2.2%
O1334
 
2.1%
P1327
 
2.1%
Other values (20)8088
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)64473
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I16953
26.3%
A11825
18.3%
N10011
15.5%
D7425
11.5%
C2137
 
3.3%
E2124
 
3.3%
H1840
 
2.9%
R1409
 
2.2%
O1334
 
2.1%
P1327
 
2.1%
Other values (20)8088
12.5%
Distinct410
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.2 MiB
2026-01-19T14:20:01.228244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length7
Mean length7.0643898
Min length6

Characters and Unicode

Total characters1181717
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)< 0.1%

Sample

1st row15-1132
2nd row25-1011
3rd row25-2052
4th row25-1042
5th row15-1133
ValueCountFrequency (%)
15-113273136
43.7%
15-112116939
 
10.1%
15-113316693
 
10.0%
13-11117984
 
4.8%
25-20313919
 
2.3%
25-20213707
 
2.2%
15-11313698
 
2.2%
15-11992729
 
1.6%
25-10712605
 
1.6%
15-20312111
 
1.3%
Other values (400)33757
20.2%
2026-01-19T14:20:01.615538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1480566
40.7%
-167237
 
14.2%
2159998
 
13.5%
5154192
 
13.0%
3137042
 
11.6%
045056
 
3.8%
917911
 
1.5%
45525
 
0.5%
74941
 
0.4%
64857
 
0.4%
Other values (2)4392
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1181717
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1480566
40.7%
-167237
 
14.2%
2159998
 
13.5%
5154192
 
13.0%
3137042
 
11.6%
045056
 
3.8%
917911
 
1.5%
45525
 
0.5%
74941
 
0.4%
64857
 
0.4%
Other values (2)4392
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1181717
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1480566
40.7%
-167237
 
14.2%
2159998
 
13.5%
5154192
 
13.0%
3137042
 
11.6%
045056
 
3.8%
917911
 
1.5%
45525
 
0.5%
74941
 
0.4%
64857
 
0.4%
Other values (2)4392
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1181717
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1480566
40.7%
-167237
 
14.2%
2159998
 
13.5%
5154192
 
13.0%
3137042
 
11.6%
045056
 
3.8%
917911
 
1.5%
45525
 
0.5%
74941
 
0.4%
64857
 
0.4%
Other values (2)4392
 
0.4%
Distinct561
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.4 MiB
2026-01-19T14:20:01.839927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length78
Median length75
Mean length32.954913
Min length6

Characters and Unicode

Total characters5512632
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique136 ?
Unique (%)0.1%

Sample

1st rowSoftware Developers, Applications
2nd rowBusiness Teachers, Postsecondary
3rd rowSpecial Education Teachers, Kindergarten and Eleme
4th rowBiological Science Teachers, Postsecondary
5th rowSoftware Developers, Systems Software
ValueCountFrequency (%)
software108942
19.1%
developers90165
15.8%
applications74423
13.1%
systems34817
 
6.1%
analysts29299
 
5.1%
teachers29211
 
5.1%
computer27988
 
4.9%
postsecondary14623
 
2.6%
and13102
 
2.3%
special12188
 
2.1%
Other values (368)134272
23.6%
2026-01-19T14:20:02.216756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e493769
 
9.0%
401753
 
7.3%
s308680
 
5.6%
o294308
 
5.3%
S284668
 
5.2%
a281011
 
5.1%
t276194
 
5.0%
r254882
 
4.6%
p220955
 
4.0%
A200772
 
3.6%
Other values (50)2495640
45.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)5512632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e493769
 
9.0%
401753
 
7.3%
s308680
 
5.6%
o294308
 
5.3%
S284668
 
5.2%
a281011
 
5.1%
t276194
 
5.0%
r254882
 
4.6%
p220955
 
4.0%
A200772
 
3.6%
Other values (50)2495640
45.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5512632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e493769
 
9.0%
401753
 
7.3%
s308680
 
5.6%
o294308
 
5.3%
S284668
 
5.2%
a281011
 
5.1%
t276194
 
5.0%
r254882
 
4.6%
p220955
 
4.0%
A200772
 
3.6%
Other values (50)2495640
45.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5512632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e493769
 
9.0%
401753
 
7.3%
s308680
 
5.6%
o294308
 
5.3%
S284668
 
5.2%
a281011
 
5.1%
t276194
 
5.0%
r254882
 
4.6%
p220955
 
4.0%
A200772
 
3.6%
Other values (50)2495640
45.3%
Distinct57
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.5 MiB
2026-01-19T14:20:02.386900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length24
Median length20
Mean length8.9171499
Min length4

Characters and Unicode

Total characters1491643
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowIllinois
2nd rowCalifornia
3rd rowCalifornia
4th rowTexas
5th rowOregon
ValueCountFrequency (%)
california46782
23.6%
new22573
 
11.4%
texas15498
 
7.8%
york11373
 
5.7%
jersey10198
 
5.1%
illinois7411
 
3.7%
massachusetts6848
 
3.5%
virginia6338
 
3.2%
georgia5615
 
2.8%
pennsylvania4725
 
2.4%
Other values (54)61071
30.8%
2026-01-19T14:20:02.681518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a198558
13.3%
i187697
12.6%
n120397
 
8.1%
o116673
 
7.8%
r108632
 
7.3%
e91442
 
6.1%
s91029
 
6.1%
l83827
 
5.6%
C56218
 
3.8%
f48152
 
3.2%
Other values (36)389018
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1491643
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a198558
13.3%
i187697
12.6%
n120397
 
8.1%
o116673
 
7.8%
r108632
 
7.3%
e91442
 
6.1%
s91029
 
6.1%
l83827
 
5.6%
C56218
 
3.8%
f48152
 
3.2%
Other values (36)389018
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1491643
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a198558
13.3%
i187697
12.6%
n120397
 
8.1%
o116673
 
7.8%
r108632
 
7.3%
e91442
 
6.1%
s91029
 
6.1%
l83827
 
5.6%
C56218
 
3.8%
f48152
 
3.2%
Other values (36)389018
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1491643
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a198558
13.3%
i187697
12.6%
n120397
 
8.1%
o116673
 
7.8%
r108632
 
7.3%
e91442
 
6.1%
s91029
 
6.1%
l83827
 
5.6%
C56218
 
3.8%
f48152
 
3.2%
Other values (36)389018
26.1%
Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.4 MiB
2026-01-19T14:20:02.815330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters334556
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowIL
2nd rowCA
3rd rowCA
4th rowTX
5th rowOR
ValueCountFrequency (%)
ca46782
28.0%
tx15498
 
9.3%
ny11373
 
6.8%
nj10198
 
6.1%
il7411
 
4.4%
ma6848
 
4.1%
va6031
 
3.6%
ga5615
 
3.4%
pa4725
 
2.8%
wa4610
 
2.8%
Other values (46)48187
28.8%
2026-01-19T14:20:03.071202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A80259
24.0%
C56218
16.8%
N32134
9.6%
M20111
 
6.0%
T19559
 
5.8%
I16310
 
4.9%
X15498
 
4.6%
L12917
 
3.9%
Y12059
 
3.6%
J10198
 
3.0%
Other values (14)59293
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)334556
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A80259
24.0%
C56218
16.8%
N32134
9.6%
M20111
 
6.0%
T19559
 
5.8%
I16310
 
4.9%
X15498
 
4.6%
L12917
 
3.9%
Y12059
 
3.6%
J10198
 
3.0%
Other values (14)59293
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)334556
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A80259
24.0%
C56218
16.8%
N32134
9.6%
M20111
 
6.0%
T19559
 
5.8%
I16310
 
4.9%
X15498
 
4.6%
L12917
 
3.9%
Y12059
 
3.6%
J10198
 
3.0%
Other values (14)59293
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)334556
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A80259
24.0%
C56218
16.8%
N32134
9.6%
M20111
 
6.0%
T19559
 
5.8%
I16310
 
4.9%
X15498
 
4.6%
L12917
 
3.9%
Y12059
 
3.6%
J10198
 
3.0%
Other values (14)59293
17.7%

WORK_POSTAL_CODE
Text

Missing 

Distinct6332
Distinct (%)11.8%
Missing113604
Missing (%)67.9%
Memory size6.7 MiB
2026-01-19T14:20:03.312636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length12
Median length5
Mean length5.4244699
Min length4

Characters and Unicode

Total characters291153
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2617 ?
Unique (%)4.9%

Sample

1st row94132.0
2nd row07417
3rd row83209
4th row11205
5th row85713
ValueCountFrequency (%)
940432034
 
3.8%
98052772
 
1.4%
95134.0747
 
1.4%
94043.0666
 
1.2%
94105646
 
1.2%
95054572
 
1.1%
94107501
 
0.9%
94103451
 
0.8%
92121407
 
0.8%
98004352
 
0.7%
Other values (6322)46526
86.7%
2026-01-19T14:20:03.687743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
063094
21.7%
132883
11.3%
430824
10.6%
925868
8.9%
225846
8.9%
324682
 
8.5%
522357
 
7.7%
719838
 
6.8%
819324
 
6.6%
614966
 
5.1%
Other values (2)11471
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)291153
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
063094
21.7%
132883
11.3%
430824
10.6%
925868
8.9%
225846
8.9%
324682
 
8.5%
522357
 
7.7%
719838
 
6.8%
819324
 
6.6%
614966
 
5.1%
Other values (2)11471
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)291153
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
063094
21.7%
132883
11.3%
430824
10.6%
925868
8.9%
225846
8.9%
324682
 
8.5%
522357
 
7.7%
719838
 
6.8%
819324
 
6.6%
614966
 
5.1%
Other values (2)11471
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)291153
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
063094
21.7%
132883
11.3%
430824
10.6%
925868
8.9%
225846
8.9%
324682
 
8.5%
522357
 
7.7%
719838
 
6.8%
819324
 
6.6%
614966
 
5.1%
Other values (2)11471
 
3.9%

FULL_TIME_POSITION_Y_N
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing11093
Missing (%)6.6%
Memory size326.8 KiB
True
152594 
False
 
3591
(Missing)
 
11093
ValueCountFrequency (%)
True152594
91.2%
False3591
 
2.1%
(Missing)11093
 
6.6%
2026-01-19T14:20:03.810745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

VISA_CLASS
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.8 MiB
H-1B
154497 
greencard
 
11093
E-3 Australian
 
1393
H-1B1 Singapore
 
148
H-1B1 Chile
 
147

Length

Max length15
Median length4
Mean length4.4307321
Min length4

Characters and Unicode

Total characters741164
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowH-1B
2nd rowgreencard
3rd rowH-1B
4th rowH-1B
5th rowE-3 Australian

Common Values

ValueCountFrequency (%)
H-1B154497
92.4%
greencard11093
 
6.6%
E-3 Australian1393
 
0.8%
H-1B1 Singapore148
 
0.1%
H-1B1 Chile147
 
0.1%

Length

2026-01-19T14:20:03.905873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-19T14:20:04.027239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
h-1b154497
91.4%
greencard11093
 
6.6%
e-31393
 
0.8%
australian1393
 
0.8%
h-1b1295
 
0.2%
singapore148
 
0.1%
chile147
 
0.1%

Most occurring characters

ValueCountFrequency (%)
-156185
21.1%
1155087
20.9%
H154792
20.9%
B154792
20.9%
r23727
 
3.2%
e22481
 
3.0%
a14027
 
1.9%
n12634
 
1.7%
g11241
 
1.5%
c11093
 
1.5%
Other values (15)25105
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)741164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
-156185
21.1%
1155087
20.9%
H154792
20.9%
B154792
20.9%
r23727
 
3.2%
e22481
 
3.0%
a14027
 
1.9%
n12634
 
1.7%
g11241
 
1.5%
c11093
 
1.5%
Other values (15)25105
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)741164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
-156185
21.1%
1155087
20.9%
H154792
20.9%
B154792
20.9%
r23727
 
3.2%
e22481
 
3.0%
a14027
 
1.9%
n12634
 
1.7%
g11241
 
1.5%
c11093
 
1.5%
Other values (15)25105
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)741164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
-156185
21.1%
1155087
20.9%
H154792
20.9%
B154792
20.9%
r23727
 
3.2%
e22481
 
3.0%
a14027
 
1.9%
n12634
 
1.7%
g11241
 
1.5%
c11093
 
1.5%
Other values (15)25105
 
3.4%
Distinct15315
Distinct (%)9.2%
Missing68
Missing (%)< 0.1%
Memory size9.9 MiB
2026-01-19T14:20:04.292240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.2547395
Min length5

Characters and Unicode

Total characters878645
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5469 ?
Unique (%)3.3%

Sample

1st row320000
2nd row289798
3rd row283628
4th row283628
5th row260000
ValueCountFrequency (%)
986751703
 
1.0%
1097621267
 
0.8%
882541105
 
0.7%
932671085
 
0.6%
80746984
 
0.6%
116605963
 
0.6%
94162932
 
0.6%
100984925
 
0.6%
97219854
 
0.5%
57949811
 
0.5%
Other values (15305)156581
93.6%
2026-01-19T14:20:04.692510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0102329
11.6%
6100389
11.4%
192148
10.5%
791615
10.4%
489589
10.2%
886016
9.8%
579589
9.1%
979087
9.0%
278640
9.0%
370729
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)878645
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0102329
11.6%
6100389
11.4%
192148
10.5%
791615
10.4%
489589
10.2%
886016
9.8%
579589
9.1%
979087
9.0%
278640
9.0%
370729
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)878645
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0102329
11.6%
6100389
11.4%
192148
10.5%
791615
10.4%
489589
10.2%
886016
9.8%
579589
9.1%
979087
9.0%
278640
9.0%
370729
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)878645
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0102329
11.6%
6100389
11.4%
192148
10.5%
791615
10.4%
489589
10.2%
886016
9.8%
579589
9.1%
979087
9.0%
278640
9.0%
370729
8.0%
Distinct20691
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size10.0 MiB
2026-01-19T14:20:04.953935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.3913964
Min length5

Characters and Unicode

Total characters901862
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12033 ?
Unique (%)7.2%

Sample

1st row62171
2nd row91440
3rd row49470
4th row43800
5th row170000
ValueCountFrequency (%)
600007650
 
4.6%
650004397
 
2.6%
700003878
 
2.3%
900003407
 
2.0%
1000003355
 
2.0%
800003348
 
2.0%
750003068
 
1.8%
1100002791
 
1.7%
850002743
 
1.6%
1050002609
 
1.6%
Other values (20681)130032
77.7%
2026-01-19T14:20:05.334325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0369344
41.0%
183646
 
9.3%
670943
 
7.9%
569818
 
7.7%
758053
 
6.4%
856058
 
6.2%
251649
 
5.7%
447943
 
5.3%
946121
 
5.1%
339840
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)901862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0369344
41.0%
183646
 
9.3%
670943
 
7.9%
569818
 
7.7%
758053
 
6.4%
856058
 
6.2%
251649
 
5.7%
447943
 
5.3%
946121
 
5.1%
339840
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)901862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0369344
41.0%
183646
 
9.3%
670943
 
7.9%
569818
 
7.7%
758053
 
6.4%
856058
 
6.2%
251649
 
5.7%
447943
 
5.3%
946121
 
5.1%
339840
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)901862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0369344
41.0%
183646
 
9.3%
670943
 
7.9%
569818
 
7.7%
758053
 
6.4%
856058
 
6.2%
251649
 
5.7%
447943
 
5.3%
946121
 
5.1%
339840
 
4.4%

JOB_TITLE_SUBGROUP
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.7 MiB
software engineer
99364 
business analyst
27811 
assistant professor
18866 
teacher
13912 
data analyst
 
3840
Other values (3)
 
3485

Length

Max length21
Median length17
Mean length16.029209
Min length7

Characters and Unicode

Total characters2681334
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsoftware engineer
2nd rowassistant professor
3rd rowteacher
4th rowteacher
5th rowsoftware engineer

Common Values

ValueCountFrequency (%)
software engineer99364
59.4%
business analyst27811
 
16.6%
assistant professor18866
 
11.3%
teacher13912
 
8.3%
data analyst3840
 
2.3%
attorney1488
 
0.9%
data scientist1227
 
0.7%
management consultant770
 
0.5%

Length

2026-01-19T14:20:05.460254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-19T14:20:05.597478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
software99364
31.1%
engineer99364
31.1%
analyst31651
 
9.9%
business27811
 
8.7%
assistant18866
 
5.9%
professor18866
 
5.9%
teacher13912
 
4.4%
data5067
 
1.6%
attorney1488
 
0.5%
scientist1227
 
0.4%
Other values (2)1540
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e476212
17.8%
s312002
11.6%
n282851
10.5%
r251860
9.4%
a228242
8.5%
t195466
7.3%
151878
 
5.7%
i148495
 
5.5%
o139354
 
5.2%
f118230
 
4.4%
Other values (11)376744
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2681334
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e476212
17.8%
s312002
11.6%
n282851
10.5%
r251860
9.4%
a228242
8.5%
t195466
7.3%
151878
 
5.7%
i148495
 
5.5%
o139354
 
5.2%
f118230
 
4.4%
Other values (11)376744
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2681334
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e476212
17.8%
s312002
11.6%
n282851
10.5%
r251860
9.4%
a228242
8.5%
t195466
7.3%
151878
 
5.7%
i148495
 
5.5%
o139354
 
5.2%
f118230
 
4.4%
Other values (11)376744
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2681334
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e476212
17.8%
s312002
11.6%
n282851
10.5%
r251860
9.4%
a228242
8.5%
t195466
7.3%
151878
 
5.7%
i148495
 
5.5%
o139354
 
5.2%
f118230
 
4.4%
Other values (11)376744
14.1%

order
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct167278
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83714.716
Minimum1
Maximum167361
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2026-01-19T14:20:05.741270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8401.85
Q141901.25
median83722.5
Q3125541.75
95-th percentile158997.15
Maximum167361
Range167360
Interquartile range (IQR)83640.5

Descriptive statistics

Standard deviation48300.236
Coefficient of variation (CV)0.57696231
Kurtosis-1.1996064
Mean83714.716
Median Absolute Deviation (MAD)41820.5
Skewness-0.0005330863
Sum1.400363 × 1010
Variance2.3329128 × 109
MonotonicityStrictly increasing
2026-01-19T14:20:05.865328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
1116161
 
< 0.1%
1115981
 
< 0.1%
1115991
 
< 0.1%
1116001
 
< 0.1%
1116011
 
< 0.1%
1116021
 
< 0.1%
1116031
 
< 0.1%
1116041
 
< 0.1%
1116051
 
< 0.1%
Other values (167268)167268
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
1673611
< 0.1%
1673601
< 0.1%
1673591
< 0.1%
1673581
< 0.1%
1673571
< 0.1%
1673561
< 0.1%
1673551
< 0.1%
1673541
< 0.1%
1673531
< 0.1%
1673521
< 0.1%

Interactions

2026-01-19T14:19:52.056778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T14:19:51.815091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T14:19:52.177990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2026-01-19T14:19:51.939062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2026-01-19T14:20:05.971145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
CASE_STATUSEDUCATION_LEVEL_REQUIREDEXPERIENCE_REQUIRED_NUM_MONTHSEXPERIENCE_REQUIRED_Y_NFULL_TIME_POSITION_Y_NJOB_TITLE_SUBGROUPPAID_WAGE_SUBMITTED_UNITPREVAILING_WAGE_SUBMITTED_UNITVISA_CLASSorder
CASE_STATUS1.0000.0990.0290.1130.0260.0750.0370.0390.2680.059
EDUCATION_LEVEL_REQUIRED0.0991.0000.3400.2980.0000.4490.0000.0001.0000.282
EXPERIENCE_REQUIRED_NUM_MONTHS0.0290.3401.0000.0590.0000.0820.0000.0001.000-0.550
EXPERIENCE_REQUIRED_Y_N0.1130.2980.0591.0000.0000.3320.0000.0001.0000.326
FULL_TIME_POSITION_Y_N0.0260.0000.0000.0001.0000.1780.6420.6390.0030.099
JOB_TITLE_SUBGROUP0.0750.4490.0820.3320.1781.0000.1030.0990.0610.298
PAID_WAGE_SUBMITTED_UNIT0.0370.0000.0000.0000.6420.1031.0000.8580.0420.058
PREVAILING_WAGE_SUBMITTED_UNIT0.0390.0000.0000.0000.6390.0990.8581.0000.0330.056
VISA_CLASS0.2681.0001.0001.0000.0030.0610.0420.0331.0000.096
order0.0590.282-0.5500.3260.0990.2980.0580.0560.0961.000

Missing values

2026-01-19T14:19:52.581792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-19T14:19:53.289292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-19T14:19:54.076245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CASE_NUMBERCASE_STATUSCASE_RECEIVED_DATEDECISION_DATEEMPLOYER_NAMEPREVAILING_WAGE_SUBMITTEDPREVAILING_WAGE_SUBMITTED_UNITPAID_WAGE_SUBMITTEDPAID_WAGE_SUBMITTED_UNITJOB_TITLEWORK_CITYEDUCATION_LEVEL_REQUIREDCOLLEGE_MAJOR_REQUIREDEXPERIENCE_REQUIRED_Y_NEXPERIENCE_REQUIRED_NUM_MONTHSCOUNTRY_OF_CITIZENSHIPPREVAILING_WAGE_SOC_CODEPREVAILING_WAGE_SOC_TITLEWORK_STATEWORK_STATE_ABBREVIATIONWORK_POSTAL_CODEFULL_TIME_POSITION_Y_NVISA_CLASSPREVAILING_WAGE_PER_YEARPAID_WAGE_PER_YEARJOB_TITLE_SUBGROUPorder
0I-200-14073-248840denied3/14/20143/21/2014ADVANCED TECHNOLOGY GROUP USA, INC.6217100year62171yearSOFTWARE ENGINEERBLOOMINGTONNaNNaNNaNNaNNaN15-1132Software Developers, ApplicationsIllinoisILNaNyH-1BNaN62171software engineer1
1A-15061-55212denied3/19/20153/19/2015SAN FRANCISCO STATE UNIVERSITY5067600year91440yearAssistant Professor of MarketingSAN FRANCISCODoctoratemarketingnNaNIRAN25-1011Business Teachers, PostsecondaryCaliforniaCA94132.0NaNgreencardNaN91440assistant professor2
2I-200-13256-001092denied9/13/20139/23/2013CAROUSEL SCHOOL4947000year49470yearSPECIAL EDUCATION TEACHERLOS ANGELESNaNNaNNaNNaNNaN25-2052Special Education Teachers, Kindergarten and ElemeCaliforniaCANaNyH-1BNaN49470teacher3
3I-200-14087-353657denied3/28/20144/7/2014HARLINGEN CONSOLIDATED INDEPENDENT SCHOOL DISTRICT251052.00month43800yearSCIENCE TEACHERHARLINGEN CISDNaNNaNNaNNaNNaN25-1042Biological Science Teachers, PostsecondaryTexasTXNaNyH-1BNaN43800teacher4
4I-203-14259-128844denied9/16/20149/23/2014SIGNAL SCIENCES CORPORATION84573.00bi-weekly170000yearSENIOR SOFTWARE ENGINEERPORTLANDNaNNaNNaNNaNNaN15-1133Software Developers, Systems SoftwareOregonORNaNyE-3 AustralianNaN170000software engineer5
5I-200-14092-483272denied4/2/20144/9/2014CAPGEMINI U.S. LLC113610month114421yearORACLE SCM ANALYST/BUSINESS ANALYSTSOUTH SAN FRANCISCONaNNaNNaNNaNNaN15-1121Computer Systems AnalystsCaliforniaCANaNyH-1BNaN114421business analyst6
6I-200-13084-487292denied3/25/20133/28/2013PURE STORAGE, INC.1333328year145000yearSENIOR SOFTWARE ENGINEERMOUNTAIN VIEWNaNNaNNaNNaNNaN15-1132Software Developers, ApplicationsCaliforniaCANaNyH-1BNaN145000software engineer7
7I-200-13126-805026denied5/6/20135/8/2013POLMAK, INC.104458month104458yearSOFTWARE ENGINEERRIDGEFIELD PARKNaNNaNNaNNaNNaN15-1132Software Developers, ApplicationsNew JerseyNJNaNyH-1BNaN104458software engineer8
8I-200-13128-133480denied5/10/20135/14/2013GOOGLE INC.1212002.00year160000yearSOFTWARE ENGINEERNEW YORK CITYNaNNaNNaNNaNNaN15-1132Software Developers, ApplicationsNew YorkNYNaNyH-1BNaN160000software engineer9
9I-200-14069-400950denied3/10/20143/18/2014STLPORT CONSULTING, INC.98675.00month98675yearSOFTWARE ENGINEERMOUNTAIN VIEWNaNNaNNaNNaNNaN15-1132Software Developers, ApplicationsCaliforniaCANaNyH-1BNaN98675software engineer10
CASE_NUMBERCASE_STATUSCASE_RECEIVED_DATEDECISION_DATEEMPLOYER_NAMEPREVAILING_WAGE_SUBMITTEDPREVAILING_WAGE_SUBMITTED_UNITPAID_WAGE_SUBMITTEDPAID_WAGE_SUBMITTED_UNITJOB_TITLEWORK_CITYEDUCATION_LEVEL_REQUIREDCOLLEGE_MAJOR_REQUIREDEXPERIENCE_REQUIRED_Y_NEXPERIENCE_REQUIRED_NUM_MONTHSCOUNTRY_OF_CITIZENSHIPPREVAILING_WAGE_SOC_CODEPREVAILING_WAGE_SOC_TITLEWORK_STATEWORK_STATE_ABBREVIATIONWORK_POSTAL_CODEFULL_TIME_POSITION_Y_NVISA_CLASSPREVAILING_WAGE_PER_YEARPAID_WAGE_PER_YEARJOB_TITLE_SUBGROUPorder
167268I-203-15014-844277denied1/14/20151/22/2015RESCUE RESPONSE GEAR INC.1000month1000monthTEACHER AND INSTRUCTORBENDNaNNaNNaNNaNNaN25-3099TEACHERS AND INSTRUCTORS, ALL OTHEROregonOR97701nE-3 Australian1200012000teacher167352
167269I-200-14071-935389denied3/12/20143/20/2014SACRED HEART SCHOOL11500.00year22800yearRELIGION TEACHERDEL RIONaNNaNNaNNaNNaN21-2021Directors, Religious Activities and EducationTexasTXNaNyH-1B1150022800teacher167353
167270I-200-12241-089395certified-withdrawn8/28/20126/6/2013CHINESE BIBLE CHURCH INTERNATIONAL, INC.5.05hour5.6hourMIDDLE SCHOOL TEACHERSSAIPANNaNNaNNaNNaNNaN25-2022Middle School Teachers, Except Special and Career/Northern Mariana IslandsMPNaNyH-1B1050411648teacher167354
167271I-200-12241-745406certified-withdrawn8/28/20126/6/2013CHINESE BIBLE CHURCH INTERNATIONAL, INC.5.05hour5.6hourMIDDLE SCHOOL TEACHERSSAIPANNaNNaNNaNNaNNaN25-2022Middle School Teachers, Except Special and Career/Northern Mariana IslandsMPNaNyH-1B1050411648teacher167355
167272I-200-12241-762461certified-withdrawn8/28/20126/6/2013CHINESE BIBLE CHURCH INTERNATIONAL, INC.5.05hour5.6hourMIDDLE SCHOOL TEACHERSSAIPANNaNNaNNaNNaNNaN25-2022Middle School Teachers, Except Special and Career/Northern Mariana IslandsMPNaNyH-1B1050411648teacher167356
167273I-200-12241-209885certified-withdrawn8/28/20126/6/2013CHINESE BIBLE CHURCH INTERNATIONAL, INC.5.05hour5.6hourMIDDLE SCHOOL TEACHERSSAIPANNaNNaNNaNNaNNaN25-2022Middle School Teachers, Except Special and Career/Northern Mariana IslandsMPNaNyH-1B1050411648teacher167357
167274I-200-11305-143547denied11/1/201111/3/2011CHINESE BIBLE CHURCH INTERNATIONAL, INC.5,05hour5,25hourPRESCHOOL TEACHERSAIPANNaNNaNNaNNaNNaN25-2011Preschool Teachers, Except Special EducationNorthern Mariana IslandsMPNaNyH-1B1050410920teacher167358
167275I-200-11313-833007certified11/9/201111/16/2011CHINESE BIBLE CHURCH INTERNATIONAL, INC.5,05hour5,25hourTEACHERSAIPANNaNNaNNaNNaNNaN25-3999Teachers and Instructors, All Other*Northern Mariana IslandsMPNaNyH-1B1050410920teacher167359
167276I-200-11312-798611denied11/8/201111/15/2011CHINESE BIBLE CHURCH INTERNATIONAL, INC.5,05hour5,1hourPRESCHOOL TEACHERSAIPANNaNNaNNaNNaNNaN25-2011Preschool Teachers, Except Special EducationNorthern Mariana IslandsMPNaNyH-1B1050410608teacher167360
167277I-200-11297-523711denied10/24/201110/26/2011CHINESE BIBLE CHURCH INTERNATIONAL, INC.5,05hour5,05hourPRESCHOOL TEACHERSAIPANNaNNaNNaNNaNNaN25-2011Preschool Teachers, Except Special EducationNorthern Mariana IslandsMPNaNyH-1B1050410504teacher167361